AffinityPropagation (original) (raw)
class sklearn.cluster.AffinityPropagation(*, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False, random_state=None)[source]#
Perform Affinity Propagation Clustering of data.
Read more in the User Guide.
Parameters:
dampingfloat, default=0.5
Damping factor in the range [0.5, 1.0) is the extent to which the current value is maintained relative to incoming values (weighted 1 - damping). This in order to avoid numerical oscillations when updating these values (messages).
max_iterint, default=200
Maximum number of iterations.
convergence_iterint, default=15
Number of iterations with no change in the number of estimated clusters that stops the convergence.
copybool, default=True
Make a copy of input data.
preferencearray-like of shape (n_samples,) or float, default=None
Preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, ie of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities.
affinity{‘euclidean’, ‘precomputed’}, default=’euclidean’
Which affinity to use. At the moment ‘precomputed’ andeuclidean are supported. ‘euclidean’ uses the negative squared euclidean distance between points.
verbosebool, default=False
Whether to be verbose.
random_stateint, RandomState instance or None, default=None
Pseudo-random number generator to control the starting state. Use an int for reproducible results across function calls. See the Glossary.
Added in version 0.23: this parameter was previously hardcoded as 0.
Attributes:
**cluster_centers_indices_**ndarray of shape (n_clusters,)
Indices of cluster centers.
**cluster_centers_**ndarray of shape (n_clusters, n_features)
Cluster centers (if affinity != precomputed).
**labels_**ndarray of shape (n_samples,)
Labels of each point.
**affinity_matrix_**ndarray of shape (n_samples, n_samples)
Stores the affinity matrix used in fit.
**n_iter_**int
Number of iterations taken to converge.
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
Notes
For an example usage, see Demo of affinity propagation clustering algorithm.
The algorithmic complexity of affinity propagation is quadratic in the number of points.
When the algorithm does not converge, it will still return a arrays ofcluster_center_indices and labels if there are any exemplars/clusters, however they may be degenerate and should be used with caution.
When fit does not converge, cluster_centers_ is still populated however it may be degenerate. In such a case, proceed with caution. If fit does not converge and fails to produce any cluster_centers_then predict will label every sample as -1.
When all training samples have equal similarities and equal preferences, the assignment of cluster centers and labels depends on the preference. If the preference is smaller than the similarities, fit will result in a single cluster center and label 0 for every sample. Otherwise, every training sample becomes its own cluster center and is assigned a unique label.
References
Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007
Examples
from sklearn.cluster import AffinityPropagation import numpy as np X = np.array([[1, 2], [1, 4], [1, 0], ... [4, 2], [4, 4], [4, 0]]) clustering = AffinityPropagation(random_state=5).fit(X) clustering AffinityPropagation(random_state=5) clustering.labels_ array([0, 0, 0, 1, 1, 1]) clustering.predict([[0, 0], [4, 4]]) array([0, 1]) clustering.cluster_centers_ array([[1, 2], [4, 2]])
Fit the clustering from features, or affinity matrix.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse feature matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored
Not used, present here for API consistency by convention.
Returns:
self
Returns the instance itself.
fit_predict(X, y=None)[source]#
Fit clustering from features/affinity matrix; return cluster labels.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse feature matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored
Not used, present here for API consistency by convention.
Returns:
labelsndarray of shape (n_samples,)
Cluster labels.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
get_params(deep=True)[source]#
Get parameters for this estimator.
Parameters:
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
paramsdict
Parameter names mapped to their values.
Predict the closest cluster each sample in X belongs to.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.
Returns:
labelsndarray of shape (n_samples,)
Cluster labels.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Parameters:
**paramsdict
Estimator parameters.
Returns:
selfestimator instance
Estimator instance.